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Risk assessment for credit portfolios: A coupled Markov chain model

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  • Kaniovski, Y.M.
  • Pflug, G.Ch.

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  • Kaniovski, Y.M. & Pflug, G.Ch., 2007. "Risk assessment for credit portfolios: A coupled Markov chain model," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2303-2323, August.
  • Handle: RePEc:eee:jbfina:v:31:y:2007:i:8:p:2303-2323
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    References listed on IDEAS

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    1. Kahle, Kathleen M. & Walkling, Ralph A., 1996. "The Impact of Industry Classifications on Financial Research," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 31(3), pages 309-335, September.
    2. M. Davis & V. Lo, 2001. "Infectious defaults," Quantitative Finance, Taylor & Francis Journals, vol. 1(4), pages 382-387.
    3. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
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    Citations

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    Cited by:

    1. Wozabal, David & Hochreiter, Ronald, 2012. "A coupled Markov chain approach to credit risk modeling," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 403-415.
    2. D. V. Boreiko & Y. M. Kaniovski & G. Ch. Pflug, 2016. "Modeling dependent credit rating transitions: a comparison of coupling schemes and empirical evidence," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 24(4), pages 989-1007, December.
    3. W. Scott Frame & Lawrence J. White, 2009. "Technological Change, Financial Innovation, and Diffusion in Banking," Working Papers 09-03, New York University, Leonard N. Stern School of Business, Department of Economics.
    4. Serguei Kaniovski, 2008. "The exact bias of the Banzhaf measure of power when votes are neither equiprobable nor independent," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 31(2), pages 281-300, August.
    5. W. Hölzl & S. Kaniovski & Y. Kaniovski, 2019. "Exploring the dynamics of business survey data using Markov models," Computational Management Science, Springer, vol. 16(4), pages 621-649, October.
    6. T. Gärtner & S. Kaniovski & Y. Kaniovski, 2021. "Numerical estimates of risk factors contingent on credit ratings," Computational Management Science, Springer, vol. 18(4), pages 563-589, October.
    7. Serguei Kaniovski, 2010. "Aggregation of correlated votes and Condorcet’s Jury Theorem," Theory and Decision, Springer, vol. 69(3), pages 453-468, September.
    8. D. V. Boreiko & Y. M. Kaniovski & G. Ch. Pflug, 2017. "Numerical Modeling of Dependent Credit Rating Transitions with Asynchronously Moving Industries," Computational Economics, Springer;Society for Computational Economics, vol. 49(3), pages 499-516, March.
    9. Dmitri Boreiko & Serguei Kaniovski & Yuri Kaniovski & Georg Ch. Pflug, 2018. "Business Cycles and Conditional Credit-Rating Migration Matrices," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 1-19, December.
    10. Jeffrey R. Stokes, 2023. "A nonlinear inversion procedure for modeling the effects of economic factors on credit risk migration," Review of Quantitative Finance and Accounting, Springer, vol. 61(3), pages 855-878, October.

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